Monitoring system and method for verifying measurements in pattened structures

ABSTRACT

A method and system are presented for monitoring measurement of parameters of patterned structures based on a predetermined fitting model. The method comprises: (a) providing data indicative of measurements in at least one patterned structure; and (b) applying at least one selected verification mode to said data indicative of measurements, said at least one verification mode comprising: I) analyzing the data based on at least one predetermined factor and classifying the corresponding measurement result as acceptable or unacceptable, II) analyzing the data corresponding to the unacceptable measurement results and determining whether one or more of the measurements providing said unacceptable result are to be disregarded, or whether one or more parameters of the predetermined fitting model are to be modified.

TECHNOLOGICAL FIELD AND BACKGROUND

Optical Critical Dimension (also termed “optical CD” or “OCD”)measurement techniques (known also as Scatterometry) are known asefficient techniques for measuring parameters of patterned (periodic)structures. Measurement of these parameters provides a viable metrologysolution for process control in high volume manufacturing ofsemiconductor devices.

OCD measurements are usually performed using a fitting procedure.According to this procedure, theoretical model describing a structureunder measurements is used to generate theoretical data or referencedata, and the latter is iteratively compared to measured data, whilevarying the model parameters until the “best fit” is found. Theparameters of the “best fit” model are considered as corresponding tothe measured parameters. Measured data (typically optical data) can beanalyzed to derive information regarding the geometrical parameters ofpatterns including thicknesses, critical dimension (CD), line spacing,line width, wall depth, wall profile, etc., as well as optical constantsof materials included in a sample being measured.

Optical metrology tools used for such measurements are typicallyellipsometry and/or reflectometry based tools. Reflectometry based toolstypically measure changes in the magnitude of radiation, whetherun-polarized or polarized returned/transmitted from/through the sample,and ellipsometry based tools typically measure changes of thepolarization state of radiation after interacting with the sample. Inaddition or as alternative to these techniques, angular analysis oflight returned (reflected and/or scattered) from a patterned (periodic)structure could be used to measure the parameters thatdefine/characterize the structure.

GENERAL DESCRIPTION

While optical CD has proven its process control benefits, it suffersfrom perceptual credibility drawback relative to image-based metrologytechniques, which are considered more direct hence more credible. One ofthe reasons is that measured spectra are not readily interpreted by thehuman eye (as opposed to microscopy images). Hence, possible errors indata (e.g. due to the measurement tool malfunction), might be unnoticedand harm the model fitting. Another reason is that the scatterometrytheoretical modeling usually contains prior assumptions, such as thatsome semiconductor materials are stable and known, that certaingeometrical parameters can be assumed fixed, etc. Deviations of realityfrom these assumptions will induce some error in the fitting process andhence in the measurement results, and in most cases such error is noteasily identified or quantified. The potential damage of these errors issuboptimal control of the manufacturing process possibly creating asemiconductor yield issue.

There is thus a need in the art for a novel technique for measuringparameter(s) of a patterned structure, utilizing verification ofmeasurements to determine as to whether disregard certain measurement,or whether modify one or more parameters of a fitting model. In thisconnection, it should be understood that the present invention providesverification of measurements by applying one or more verification modesto data indicative of measurements, where such data may include rawmeasured data (i.e. before fitting) or measurement health data; and/ormeasured data corresponding to a desired degree of fit with the model;and/or measurement/metrology result (i.e. structure parameter(s)calculated from the fitting procedure). At times, in the descriptionbelow, all these categories of data indicative of measurements arereferred to as “measured data”, but this term should be interpretedcorrectly as defined above.

The present invention provides several key performance metrics thattogether constitute a verification methodology that can act as asafeguard against scatterometry errors of different types and can flagpotentially erroneous measurement results. The invention is based on theunderstanding of the following. As indicated above, scatterometry istypically based on fitting a theoretical (simulated) model data orsignal to a measured spectral (diffraction) signature from a patterned(periodic) structure. Currently, during setup (off-line), theoreticalmodel is tested and matched to all available measurement samples, i.e.for calculation accuracy and/or geometrical description and/ormaterials, etc. During actual (on-line) measurements of new unknownsamples with set-up model, there still can be several possible factorsthat could potentially cause an error in measurements. These factors maybe associated with a mismatch between theoretical model and measurementsample, such as deficiency of geometrical description of stackstructures (e.g. missing layer, absence of corner rounding, etc.),inaccurate model of material optical properties (e.g. materialvariability not taken into account), assumption of nominal fixed valuefor some stack parameter is not true for the sample (different constantvalue or variable value). Also, these factors may be associated withmeasurement data inaccuracies including systematic measurement errors(e.g. due to problem in at least one of calibration, patternrecognition, focus and/or other measurement tool sub-systems), level ofrandom noise beyond the acceptance level for the particular tool.

The present invention provides for detection of scatterometry erroneousmeasurements (on the metrology tool). This could be achieved byintroduction of a verification module including at least one errorindicator (EI) utility, or combinations of multiple error indicatorutilities each one designed to flag one or more potential measurementproblems of different types. The output data of multiple El's could befurther combined into a single Verification Figure of Merit (VFM), e.g.termed “score”, characterizing the quality of the measurement. Byplacing a control limit (threshold) on VFM the system could flag out anyparticular measurement that does not comply with required measurementquality and the scatterometry metrology tool can be used in a safer,more reliable way. The reason for flagged measurements can be furtheranalyzed based on EI information allowing user to decide whethercorrection action is required or results of the measurements can be usedfor the purpose of process control.

Error indicators can be based on any single measurement as well as onwafer statistics. For all error indicators, confidence and score limitscan be set during the recipe setup (off-line) steps. These limits arepart of the measurement recipe and are used for calculation ofverification figure of merit when recipe is used for productionmeasurements.

Thus, according to one broad aspect of the invention, there is provideda method for monitoring measurement of parameters of patternedstructures, said measurement of the parameters of patterned structuresbeing based on a predetermined fitting model, the method comprising:

(a) providing data indicative of measurements in at least one patternedstructure, to enable determination of at least one parameter of thepatterned structure;

(b) applying at least one selected verification mode to said dataindicative of measurements said at least one verification modecomprising: analyzing the data based on at least one predeterminedfactor and classifying corresponding measurement result as acceptable orunacceptable, thereby enabling to determine whether one or more of themeasurements providing said unacceptable result are to be disregarded,or whether one or more parameters of the predetermined fitting model areto be modified.

As indicated above, the data indicative of measurements includes atleast one of the following types of data: raw measured data, measurementhealth data, data corresponding to a desired degree of fit with thefitting model, and measurement result in the form of one or structureparameters calculated from a fitting procedure.

The desired degree of fit is typically defined by a merit function orgoodness of fit factor. In some embodiments, the application of theselected verification mode may comprise analyzing multiple values of themerit function determined for multiple measurement sites respectively,and upon determining that said multiple values of the merit functioninclude at least one value that differs from other of said multiplevalues by a value exceeding certain threshold, classifying thecorresponding measurement as unacceptable result. The multiplemeasurement sites may comprise at least one control site having aconfiguration corresponding to at least one other measurement site andbeing characterized by a smaller number of floating parameters of thestructure. In some embodiments, application of the selected verificationmode comprises analyzing at least two merit functions determined for acontrol site and at least one measurement site respectively, and upondetermining that a difference between the merit functions of the controlsite and said at least measurement site differs by a value exceedingcertain threshold, classifying the corresponding measurement as theunacceptable result. In some embodiments, the merit function is utilizedfor determining a measurement result in the form of at least oneparameter of the patterned structure, for each of a control site and atleast one measurement site.

In some embodiments, the raw measured data comprises at least two datapieces corresponding to at least two different measurement conditionsrespectively. At least one model based measured parameter correspondingto a predetermined degree of fit with the raw measured data piece may beutilized for each of said at least two data pieces, and the at least oneparameter of the patterned structure is determined. In this case, theapplication of the selected verification mode may comprise analyzing atleast two values of said at least one parameter of the patternedstructure corresponding to said at least two different measurementconditions, and upon determining that a difference between said at leasttwo values exceeds a certain threshold, classifying the correspondingmeasurement as unacceptable result. The data indicative of measurementsmay comprise spectral data, in which case the at least two data piecesmay correspond to at least two different sets of wavelengthsrespectively. In some other examples, the at least two data piecescorrespond to at least two different angles of incidence of radiationonto the structure, and/or angles of radiation propagation from thestructure, utilized in the measurements; as well as at least two datapieces correspond to at least two different polarizations of radiationutilized in the measurements.

In some embodiments, the raw measured data is in the form a multi-pointfunction of a measured response of the structure to incident radiation.The application of the selected verification mode may comprise comparingthe multi-point function of a measured response with a theoreticalmodel-based function corresponding to a predetermined degree of fit withthe measured response, to enable to determine whether said multi-pointfunction includes at least one function value for at least onemeasurement point that differs from the function values at othermeasurement points by a value exceeding certain threshold.

In some embodiments, the application of the selected verification modecomprises determining a number of iteration steps applied to reach thedesired goodness of fit condition, and upon identifying that said numberexceeds a certain threshold, classifying the corresponding measurementas the unacceptable result.

In some embodiments, said raw measured data may correspond to themeasurements performed on the same measurement site and comprisingmeasured signals successively obtained from said measurement site, andmay further comprise an integrated measured signal formed by saidserious of measured signals successively measured on the samemeasurement site. The application of the selected verification mode maycomprise comparing the measured signals with one another to determinewhether there exists at least one measured signal that differs fromother measured signals by a value exceeding certain threshold; and/orcomparing the measured signals with the integrated measured signal todetermine whether there exists at least one measured signal that differsfrom the integrated measured signal by a value exceeding certainthreshold.

According to another broad aspect of the invention, there is provided amonitoring system for controlling measurements of parameters ofpatterned structures, the system comprising:

-   -   (a) data input utility for receiving data indicative of        measurements in at least one patterned structure;    -   (b) a memory utility for storing at least one fitting model; and    -   (c) a processor utility comprising:    -   a fitting utility configured and operable for utilizing said at        least one fitting model to determine a measurement result in the        form of at least one parameter of the patterned structure;    -   a verification module comprising one or more error indicator        utilities each configured and operable for applying at least one        verification mode to data indicative of measurements, said at        least one verification mode comprising: analyzing said data        based on at least one predetermined factor and classifying        corresponding measurement as acceptable or unacceptable and        generating output data indicative thereof, thereby enabling to        determine whether one or more of the unacceptable measurements        are to be disregarded, or whether one or more parameters of the        predetermined fitting model are to be modified.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better understand the subject matter that is disclosedherein and to exemplify how it may be carried out in practice,embodiments will now be described, by way of non-limiting example only,with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram of a monitoring system of the presentinvention for controlling optical measurements in patterned structures;

FIG. 2 is a flow chart exemplifying a method of the invention forcontrolling optical measurements in patterned structures;

FIG. 3 exemplifies operation of the monitoring system of FIG. 1 in whichthe error indicator module includes a so-called quality of fit indicatorutility;

FIG. 4A shows a schematic cross section of a typical periodic structure;

FIG. 4B shows measured values of the profile height for the structure ofFIG. 4A with and without floating material properties;

FIG. 4C illustrates the merit function results for multiple points onthree measurement sites in the structure of FIG. 4A;

FIG. 4D shows measured values of the material properties of the changinglayer in the structure of FIG. 4A, as reconstructed by the fittingprocedure in the case of having floating variable;

FIG. 5 exemplifies operation of the monitoring system of FIG. 1 in whichthe error indicator module includes a so-called single measurement noiseindicator utility;

FIG. 6 exemplifies operation of the monitoring system of FIG. 1 in whichthe error indicator module includes a control site error indicator alsotermed “parallel interpretation indicator utility”;

FIG. 7 illustrates thickness measurements of the layer in which materialproperties have changed as compared to the fixed properties, as measuredon a non-patterned site (test site) in three different lots;

FIG. 8 exemplifies operation of the monitoring system of FIG. 1 in whichthe error indicator module includes a so-called differential measurement(data splitting) utility;

FIG. 9 illustrates graphically the principles of data splittingapproach;

FIG. 10 exemplifies operation of the monitoring system of FIG. 1 inwhich the error indicator module includes a so-called residual misfitanalyzer;

FIG. 11 exemplifies an embodiment of the invention where the errorindicator module includes a fit convergence metrics utility;

FIG. 12 exemplifies an embodiment of the invention where the errorindicator module utilizes the verification mode based on analysis of thetool health data; and

FIG. 13 exemplifies an embodiment of the invention where the errorindicator module determines the measured parameters confidence and scorelimits by monitoring the across wafer fingerprint.

DETAILED DESCRIPTION OF EMBODIMENTS

Referring to FIG. 1, there is schematically illustrated, by way of ablock diagram, a monitoring system 100 configured and operable forcontrolling measurements of parameters of patterned structures. Thesystem 100 operates as a data verification system, which is connectable(via wires or wireless signal transmission) to a measurement unit 102.The data verification system 100 is a computer system including interalia such functional utilities as data input/output utilities 104,memory utility 106, data processor unit 108, and possibly a display 110.The processor unit 108 includes a fitting utility 109, a structure(wafer) parameter (metrology result) calculator 111, and a verificationmodule 112 which includes one or more error indicator utilities,generally at El_(i). Each error indicator utility is configured andoperable to perform one or more verification modes with respect to dataindicative of measurements. As indicated above, data to be verifiedincludes one or more of the following: raw measured data (beforefitting), health measured data,-measured data described by a degree offit with the model (goodness of fit (GOF), merit function (MF),measurement/metrology result (i.e. structure parameter(s) calculatedfrom the fitting procedure. The error indicator utility operates toprocess/analyze the data indicative measurements (provided off-line oron-line), and generate output data (flag) indicative of one or morepotential measurement problems. In a specific but not limiting example,each error indicator utility is configured for determining/flagging aproblem of a different type. As further shown in the figure, output datafrom multiple error indicators may be combined into a singleVerification Figure of Merit utility (VFM) 114 that characterizes thequality of the measurement.

Reference is made to FIG. 2 exemplifying a flow chart 200 of a method ofthe invention carried out by the monitoring system 100. Initially, oneor more fitting models are selected and stored in the memory utility 106(as a part of recipe design, e.g. resulting from a learning mode). Thesystem receives data indicative of optical measurements carried out inat least one measurement site in one or more patterned structures (e.g.multiple lots). The data indicative of measurements may be receiveddirectly from the measurement unit (on-line mode) or may be receivedfrom a data storage device which may be that of the measurement unit ornot (off-line mode). The received data may be raw measured datacorresponding to the measurements on at least one measurement site. Thismay be a series of detected optical response signals (e.g. from the samemeasurement site), or a multi-point function of a measured response ofthe structure to incident radiation, or a spectral signature.Alternatively or additionally, the received data may include healthmeasured data, fitting data, metrology result. The processor unit 108operates to activate one or more of the error indicators Eli of theverification module to apply at least one selected verification mode tothe received data to verify said data and generate output dataindicative thereof. The verification mode includes analyzing thereceived data using one or more verification factors (threshold basedfactors), and classifying the corresponding measurement(s) as acceptableor unacceptable. Optionally, data corresponding to the unacceptablemeasurement/result can be further analyzed to determine whether one ormore measurements providing the unacceptable result are to bedisregarded, or whether one or more parameters of a fitting model are tobe modified.

The following are some examples of the configuration and operation ofthe verification module 112. It should be noted that the presentinvention is exemplified herein as relating to scatterometrymeasurements, where measured data may be in the form of spectralsignature. However, the principles of the invention are not limited tothis specific application, and the invention can generally be used withyany model-based measurement technique, namely a technique where measureddata is interpreted by model (fitting procedure) and the sample'sparameters of interest are derived from the model values correspondingto the best fit with the measured data. Also, the invention isexemplified herein as relating to measurements taken on semiconductorwafers. However, it should be understood that the invention is notlimited to this specific application, and sample/structure undermeasurements may be any patterned structure. The parameters to bemeasured may include features of the pattern (e.g. critical dimensions),as well as layers' thicknesses.

Reference is made to FIG. 3 exemplifying the operation of the monitoringsystem 100 in which the verification module 112 includes a so-calledquality of fit indicator utility. The processor unit receives dataindicative of measurements in the form of raw measured datacorresponding to the measurements on multiple measurement sites, andactivates the quality of fit indicator utility. The latter applies aselected fitting model (from the memory utility) to the raw data (i.e.performs fitting procedure), determines a merit function (constituting amodel based measured parameter corresponding to at least one parameterof the patterned structure) for each of the measurement sitescorresponding to a predetermined degree of fit with the raw data. Then,the error indicator utility operates to perform the selectedverification mode. To this end, multiple values of the merit functionfor multiple measurement sites are analyzed. If there exists at leastone merit function value that differs from other values by a valueexceeding a predetermined threshold, the error indicator utilityclassifies the corresponding measurement result as unacceptable andgenerates corresponding output data; otherwise, the processor unit mayutilize the merit functions to calculate the structure parameter(s)(metrology result).

As indicated above, fitting merit function may be computed as a functionof differences between theoretical and experimental diffractionsignatures. The fitting merit function is typically used as theminimization parameter in the fitting process, i.e. model parameters arerepeatedly modified and the fitting merit function is re-calculateduntil the fitting merit function reaches a minimum. Once the best fitbetween model and given measurement data is achieved, the final value ofthe fitting merit function represents the residual mismatch error thatcannot be described under the assumption of the current model. If theresidual value of fitting merit function is beyond the standard level asdefined at model setup, this is an indicator of possible problem, e.g.one of the model assumptions was violated in measured data.

In this connection, reference is made to FIGS. 4A to 4D. FIG. 4A shows aschematic cross section of a typical periodic structure (only threeperiods shown) on which scatterometry measurements are taken, includingmultiple uniform layers and a periodic line array of photoresist at thetop layer. The optical properties of one of the materials in the stackhave been deliberately modified in one of the measurement sites, whilethe theoretical model used the constant material properties.

FIG. 4B shows measured values of the profile height for the structure ofFIG. 4A with and without floating material properties (graphs G1 and G2respectively). Here, the measurement results are shown in graph G2,indicating that the profile height values became significantly lower inone of the measurement sites.

FIG. 4C illustrates the merit function results for multiple points onthree measurement sites. The material properties of one of the layers inthe structure have been deliberately modified for one measurement site,as indicated by the higher merit function values obtained while n&k(material properties) have been kept constant (squares S). When floatingthe n&k values, the merit function levels are recovered to the samelevels of the other two sites (diamonds D). As shown in FIG. 4C, thefitting merit function clearly indicates that its values for specificmeasurement site are significantly higher than for other sites, thuscorrectly flagging the suspicious measurements. By including variablematerial properties into the model (G1, D), the correct measurement ofthe profile height and fitting merit function values can be recovered.

FIG. 4D shows measured (relative) values of the material properties ofthe changing layer (similar to the example of FIG. 4C) as reconstructedby the fitting procedure in the case of having floating variable.

Reference is made to FIG. 5 exemplifying the operation of the monitoringsystem 100 in which the verification module 112 includes a so-calledsingle measurement noise indicator utility. The data to be verified maybe in the form of raw measured data corresponding to the measurementsperformed on the same measurement site and comprising a sequence/set ofmeasured signals successively obtained from said measurement site, or anintegrated measured signal formed by such serious of measured signals.The error indicator utility applies the verification mode to the rawmeasured data. The verification mode may include comparison between themeasured signals with one another, to determine whether there exists atleast one measured signal that differs from other measured signals by avalue exceeding certain threshold. Alternatively or additionally, theverification mode may include comparison between each of the measuredsignals and the integrated measured signal, to determine whether themeasured signals include at least one measured signal that differs fromthe integrated measured signal by a value exceeding certain threshold.

In this connection, the following should be understood. Therepeatability of the measurement results (after fitting) is probably themost used quality metric in off-line data analysis and toolqualification. Low repeatability is an indicator for possible toolissues, e.g. high noise or stability problems, but it can also indicatea change in one of the model's assumptions. If for example a parameterthat has been assumed fixed is changing, the solution becomes not onlyless accurate but also less stable, hence the repeatability of certainparameters will be downgraded. Repeatability testing procedure isusually performed only in dedicated tests, due to considerable timerequired for repeated data collection steps with production non-worthythroughput. However, there are several options to evaluate therepeatability with minimal throughput impact, if it is possible tomeasure the measured data noise. To evaluate the noise in real time, onthe real target (site, wafer), one of the following can be used.

In some situations, the data being interpreted can be (or already is)collected in a sequence of short exposures rather than a single longexposure. Typically the different exposures are averaged out to minimizenoise. However, if read separately, the short exposures can be used toevaluate the remaining noise in the averaged result. For exampleassuming Gaussian random noise, the RMS of the noise in the average willbe the RMS of the noise in each single measurement divided by the squareroot of the (known) number of single measurements in the average.

Another example is based on that in many cases a statistical indicatoris needed (e.g. for a wafer or a lot) rather than an indicator for aspecific measurement, and accordingly repeatability can also beevaluated using a bootstrapping methods. More specifically, a secondmeasurement may be collected on some of the sites (e.g. one per wafer),logging the difference between the two measurements and accumulating thedifferences along time in order to obtain a representative value for thetypical noise. If for example a full lot (25 wafers) is being measuredand a single site (die) is measured twice per wafer, a reliablemeasurement of the spectral noise can be established per lot withminimal throughput hit to the overall system.

During recipe setup the impact of spectral noise on the recipeperformance is defined with sensitivity and correlation analysis andstored in the recipe, to be later used during the measurements tocalculate impact of measured spectral noise on all floating parameters.

Reference is made to FIG. 6 exemplifying the operation of the monitoringsystem 100 in which the verification module 112 includes a control siteerror indicator also termed “parallel interpretation indicator utility”.In this example, received data includes raw measured data indicative ofoptical measurements taken on multiple (generally at least two)measurements sites including at least one test/control site. Consideringsemiconductor wafers, the test site is typically located within a marginregion of the wafer, while “actual” measurement site is located withinthe patterned region (dies' region) of the wafer.

In some embodiments, the control site indicator utility is configuredand operable to perform the verification mode with respect to themeasured data pieces of the test site and one or more actual measurementsites. It should be understood that if more than one actual measurementsite is considered, these may be actual measurements sites in the samewafer or similar sites in the wafers of different lots. In some otherembodiments, the control site indicator utility is configured andoperable to perform the verification mode with respect to the measureddata pieces of different test site located in the same wafer or similarwafers of different lots.

Thus, the processor unit receives two types/pieces of the raw measureddata including measured data from either the control/test site andmeasured data from the “actual” measurement site located in thepatterned area of the wafer, or measured data from different test sites.The processor unit operates to process the measured data pieces byapplying thereto a selected fitting model (i.e. performs fittingprocedures for each of the raw measured data pieces), and determiningthe respective merit function values (corresponding to the best fitcondition).

Then, in some embodiments, the verification mode may include comparisonbetween the merit function values for the different sites anddetermining whether a difference between them exceeds a predeterminedthreshold corresponding to unacceptable condition for the measured dataof the actual measurement site. In some other embodiments, the meritfunction values can be used for determining the respective values of acertain parameter of the structure (metrology result), and then theselected verification mode is applied to the metrology results for thedifferent sites, rather than to the merit function values, to determinewhether a difference between these parameter values exceeds apredetermined threshold, corresponding to unacceptable measured datafrom the actual measurement site.

In this connection, the following should be noted. One of the reasonsfor errors in scatterometry might relate to the ability of the fittingmodel to compensate un-modeled differences due to multiple crosscorrelated floating parameters. As a result, one may get measurementbias that might go unnoticed due to relatively good fit. In order toovercome such difficulty, additional measurement on the control site(s)may be used to validate the quality of the measurement. Such controlsites could be simpler sites which do not require as many floatingparameters, for example non-patterned (“solid”) sites in which alllayers are uniform within the area of the measurement spot in the testsite. The lower number of floating parameters can allow such sites to bemore sensitive to changes of fixed values, such as thickness ofmaterial, its optical properties and/or uniformity.

Thus, the control site error indicator may include fitting meritfunction analyzer similar to that of FIG. 3, and/or structure parameteranalyzer (metrology result analyzer) as exemplified in FIG. 6. In thelatter case, the error indicator may utilize a thresholding techniquewith respect to the common parameter values, i.e. determine whether adifference between the common parameter values in the control site andactual measurement site exceeds a predetermined threshold.

To this end, reference is made to FIG. 7 illustrating thicknessmeasurements (structure parameter) of the layer in the control site inwhich material properties are floated (graph H2) as compared tomeasurement site with the fixed material properties (graph H1), asmeasured in three different lots Lot1, Lot2 and Lot3. The differencesbetween the lots are much more pronounced for fixed n&k values.Alternatively or additionally, the error indicator may utilizeidentification of a sharp change in the values of different measurementparameters of control site(s). To minimize the TPT (throughput) hit,control site(s) sampling plan could be sparse, with one (or few) siteper wafer, assuming that control site error indicators flag the problemsthat are not local in nature.

Reference is made to FIG. 8 exemplifying the operation of the monitoringsystem 100 in which the verification module 112 includes a so-calleddifferential measurement (data splitting) utility. As shown in thefigure, the processor unit includes a data splitter utility, whichsplits raw measured data (before fitting) into two raw data pieces/partsA and B (generally at least two data pieces) corresponding respectivelyto different measurement conditions. For example these may be differentwavelengths of incident light, and/or different polarizations ofincident light and/or different angle of incidence. More specifically,the raw measured data may be in the form of a spectral signaturemeasured for a certain set of discrete wavelengths within a certainwavelength range, and the data splitter provides two spectral signatureparts of said spectral signature for respectively odd and evenwavelengths from said set of discrete wavelengths. The model fitting isapplied to each of the raw data parts, and the same structure parameter(metrology result) is calculated for the measured data parts A and B.Then, the error indicator operates to compare between these parametervalues and determined whether a difference exceeds a certain threshold.

The principles of this embodiment are associated with the following. Oneof questions that are frequently asked relates not to the mere existenceof a possible bias, but to the possible size of the bias (error) to theparameter of interest due to the issue at hand. A possible way toachieve an estimation of this effect is by looking into the consistencyof the interpretation by a method that could be termed “divide andcompare”. Usually, in all types of scatterometry tools, multiple datapoints are used together in order to establish the values of a fewfloating parameters, the number of data points much larger than thenumber of parameters (data points could be measurements taken atdifferent wavelengths, incidence angles, polarizations or anycombination). The common practice is to use all possible data points inorder to produce a measurement that will have the best repeatability andaccuracy. For the sake of verifying the accuracy and estimating theerror of the interpretation the results may be compared to somereference. Such reference may be taken from the measured data itself bysplitting the data into two parts and comparing the results. Preferably,the two data sets could have different characteristics, but similarrepeatability. As indicated above, possible realization of the splittingcould be: e.g. choosing different polarizations, splitting wavelengthrange at some point, using data from different angles, or somecombination of the above. Having split the full data set as prescribedabove it is possible now to run the interpretation three times—once withthe full data set and once with each of the split sets. While theinterpretation with the full set may be provided, a difference betweenthe two partial sets can be used as an error indication for problems inthe measurements.

It should be noted that in most cases, some difference between theresults of the two data sets might occur even under normal condition.However, the level of these differences may increase, because thedifferent parts respond differently to errors in the measurement orchanges in the model assumptions, allowing setting threshold values thatflag abnormal behavior. It should also be noted that actually theverification mode may be implemented by multiple such error indicatorscorresponding to the multiple floating parameters, allowing to selectthose parameters that are more indicative or allowing to track all ofthem, each with its own threshold level (that could be studied based ona preliminary data set during setup). Further, it should be noted thatthe error indications obtained in such verification are given in theunits of the measured parameters (usually being nanometers or degrees).Although this unit similarity does not guarantee that the errorindicators provide the real information of an error-bar of themeasurement, under proper selection of the two sets of measured data theerror indicators could be made roughly proportional up to a small factorto the real accuracy error, at least in magnitude. Also, if theinterpretation time is not a limiting factor, several splits of the samedata can be used in order to get additional error indicators, thusmaximizing the sensitivity of the entire error indicator module.

FIG. 9 illustrates graphically the principles of data splittingapproach. In the figure, the measurement of the side wall angle (SWA) ofthe pattern is shown in the form of two splits of the measureddata—graphs PI and P2, which in this not limiting example correspond todifferent polarizations of incident light, for each of three lots Lot1,Lot2, and Lot3. As shown, the difference between the two data pieces issignificant almost only for the measurements of Lot2. Thus, in thiscase, in both Lot1 and Lot3 it is clear seen that the two data sets(different polarizations in this case) agree well with one another,however in Lot2, in which material properties are incorrectly fixed, thedifferences between the values of the side wall angle measuredseparately by the two sets become significant, allowing to flag aproblem using a threshold value.

Referring to FIG. 10, there is exemplified the operation of themonitoring system 100 in which the verification module 112 includes aso-called residual misfit analyzer. According to this embodiment, asituation in which something has gone wrong is characterized byanalyzing the spectral shape of the residual (e.g. residual error vswavelength). In any realistic situation the fitting level at differentparts of the signal, e.g. different spectral bands, is different, due toall sorts of measurement accuracy issues or modeling approximations orinaccuracies. Hence, typically the residual will have a clear,significant spectral shape beyond the random noise level. Accordingly,the spectral shape of the residual can be used as a fingerprint of themeasurement, and by quantifying different parameters within thisfingerprint situations in which some abnormal behavior has occurred, theabnormality can be identified. For example, a simple method may be tosplit the data into a few parts, e.g. different spectral bands,different polarizations, etc. as described above, and calculate the meansquare error between the best fit and the measurement for each partseparately. By following over the errors in specific parts (e.g. errorin the UV part of the spectrum), or functions of them (e.g. the ratiobetween the error in the UV to the error in the IR), it is possible toobtain meaningful error indicators that can be studied during setup andcompared to a threshold for flagging abnormal behavior. Also, a moretechnically complex attitudes, such as using different transforms,moments or classification techniques for identifying a change in theresidual signal vs. the typical fingerprint learned during the setup,can also be employed.

Reference is now made to FIG. 11 exemplifying yet further embodiment ofthe invention where the verification module 112 includes a fitconvergence metrics utility. This indicator utility determines apossible mismatch between the model and the measured data at thedynamics of the convergence algorithm. The inventors have found thatwhen an error exists in the measured data or in the model, theconvergence from the initial point to the best fit takes longer thanusually, i.e. the number of iterations required is larger. Hence it ispossible to flag a potential problem whenever convergence is larger thana statistically validated number of iterations defined during recipesetup. More specifically, the processor utility operates to apply theselected fitting model to the raw measured data (measured radiationresponse of one or more measurement sites to incident radiation, wherethis fitting procedure includes one or more iteration steps until a bestfit condition is achieved. The verification mode includes analyzing anumber of the iteration steps applied to arrive to the best fitcondition, to determine whether this number exceeds a certain threshold,to thereby identify the unacceptable condition for the measured data.

Reference is now made to FIG. 12 showing yet further embodiment of theinvention where the verification mode utilizes analysis of themeasurement health data. Measurement health indicators preferably couldcover both hardware (HW) performance quality and possible mismatchbetween the sample and measurement recipe. Any measurement healthindicators can indicate a possible impact on metrology reliability.Inputs include parameters that characterize measurement system duringthe measurements, such as illumination intensity and stability, patternrecognition quality, location deviation of the measurement from thetarget (e.g. due to possible mismatch between the sample and measurementrecipe), focus quality of the measurements, etc. In that case when thequality score deteriorates below some pre-defined value, it could beused as an indication of error.

It should also be noted, that the error indicator module may beconfigured and operable to determine the measured parameters confidenceand score limits. During setup of recipe, the set of samplesrepresenting real process variations is being studied. Based on theresults statistically confidence limits are set for all measuredparameters to be later used for calculation of the score of themeasurement of production samples. Score limits are also set to indicateboundaries of the recipe or the possible range of parameters whererecipe is valid.

It should be noted that all or some of the above exemplified errorindicator utilities can be used as single site indicators and as waferstatistical indicators (wafer average, range, standard deviation, etc).There may be additional indicators that can be used on the wafer levelonly.

Referring to FIG. 13, there is exemplified the verification moduleconfigured and operable for determining the measured parametersconfidence and score limits by monitoring the across wafer fingerprint.Measured parameters often have a typical process related across-waferspatial fingerprint, e.g. constant, center-to-edge, etc. Changes in thespatial fingerprint may be used as indication of a problem. For example,a fixed parameter that has a spatial fingerprint is changing and createsa non-constant spatial fingerprint in a parameter that is usuallyuniform, as indicated by the across-wafer variation. Also, a disturbanceof the circular symmetry spatial fingerprint which is typical to manyprocess steps, can be identified by the breaking of rotational symmetry.By knowing across the wafer variations from the setup stage, it ispossible to determine whether there is a single outlier (flier)measurement, when one of the measurement points are far away from thewafer median value, or from the expected distribution or if allmeasurements on the wafer are not reliable. Thus, the measured data maybe provided in the form a multi-point function of a measured response ofthe structure to incident radiation, and the verification mode includescomparing the multi-point function of a measured response with atheoretical model-based function corresponding to a predetermined degreeof fit with the measured response, to determine whether the multi-pointfunction includes at least one function value for at least onemeasurement point that differs from the function values at othermeasurement points by a value exceeding certain threshold.

As indicated above, in order to provide a unified monitoring system allor at least some of the above exemplified error indicator utilitiescould be combined into a single verification figure of merit (VFM)available to the user. VMF can be calculated for each measurement sample(site or die), and for the wafer. The die (measurement site or die) VFMmay combine all or some error indicators related to the specificmeasurement site or die. The wafer VFM may combine together all dies'VFMs and add wafer related statistical and fingerprint error indicatorsrelated to the wafer.

Fuzzy logic approach could be used to combine all the error indicatorstogether first on the site or die level, and then on the wafer level.This may be a rule-based combination, according to which each errorindicator is assigned a threshold (or two thresholds, minimum andmaximum, as the case may be). Optionally, for each indicator a warningzone is also defined around the threshold. Each error indicator is thenassessed an assigned one of three discrete states: Pass (“green light”),fail (“red light”) or warning (“yellow light”). Having the states of allindividual error indicators, rules may be defined in the spirit of thewell known Western Electric Rules, such as, for example, if at leastthree indicators are in the red zone then the total result is “Fail”, ifat least one indicator is “Fail” and at least two indicators are“Warning” then the total result is “Warning”, etc.

In the spirit of Fuzzy logic it is possible to define a value between 0and 1 for each error indicator where values in the Fail region get 0, inthe Pass region get 1 and in-between the values are monotonously (e.g.linearly) interpolated. The total value can be obtained for example byfirst combining all the values for the different error indicators andthen comparing the result to a threshold (to define warning message).Combining the error indicators could be done based on user defineweights per each indicator, to allow user define the most important ormost relevant indicators. This type of logic could also be implementedin a multi-level fashion. For example, the error indicators are groupedtogether and the fuzzy logic value for all members of each group issummed up; for each group a threshold level is defined and a fuzzy-logicvalue is assigned based on the relation between the sum of the group andthe threshold; the group results are added and compared to threshold inorder to get the final value. The potential advantage of such a systemover a discrete rule base method could be in complex situations and incases in which seemingly contradicting information has to be evaluated.

Also, the technique based on a learning system can be used that maps thephase-space of normal error indicator values and learns how todifferentiate between normal behavior and abnormal one. Normal behaviorexamples are taken from a qualified data set achieved using a healthytool and a qualified measurement process. In order to provide a trainingset for abnormal behavior the model fixed parameters could bedeliberately shifted or the measured values could be deliberately skewedin modes similar to known hardware issues, e.g. addition of random gainor random noise. Once provided with the two data sets, the learningsystem (e.g. an artificial neural network) can be trained to separatebetween good and bad measurements. The system is then used during therun time to classify each new measurement.

We claim:
 1. A method for monitoring measurement of parameters ofpatterned structures, said measurement of the parameters of patternedstructures being based on a predetermined fitting model, the methodcomprising: (a) providing data indicative of measurements in at leastone patterned structure; wherein the data indicative of the measurementsis measurement health data; (b) applying at least one selectedverification mode to said data indicative of measurements said at leastone verification mode comprising: analyzing the data based on at leastone predetermined factor and classifying corresponding measurementresult as acceptable or unacceptable, thereby enabling to determinewhether one or more of the measurements providing said unacceptableresult are to be disregarded, or whether one or more parameters of thepredetermined fitting model are to be modified.
 2. The method of claim1, wherein the measurement health data comprises measurement healthindicators that cover hardware performance quality.
 3. The method ofclaim 1, wherein the measurement health data comprises measurementhealth indicators that cover at least one out of (a) hardwareperformance quality and (b) a mismatch between a sample that comprisesthe patterned structure and a measurement recipe that was applied duringthe measurements in at least one patterned structure.
 4. The method ofclaim 1, wherein the measurement health data comprises measurementhealth indicators that are indicative of an impact on metrologyreliability.
 5. The method of claim 1, wherein the measurement healthdata comprises measurement health indicators that characterize ameasurement system during an execution of the method.
 6. The method ofclaim 5, wherein the measurement health indicators characterize at leastone out of illumination intensity and stability.
 7. The method of claim5, wherein the measurement health indicators characterize a patternrecognition quality.
 8. The method of claim 5, wherein the measurementhealth indicators characterize a location deviation of a measurementfrom a target.
 9. The method of claim 5, wherein the measurement healthindicators characterize a focus quality of the measurements.
 10. Themethod according to claim 1 wherein the classifying comprisesclassifying the corresponding measurement result as being indicative ofan error when a quality score deteriorates below pre-defined value. 11.The method according to claim 1 wherein the classifying is executed by alearning system.
 12. The method according to claim 11 wherein thelearning system is an artificial neural network.
 13. The methodaccording to claim 11 comprising mapping, by the learning system, aphase-space of normal error indicator values; and learning, by thelearning system, to differentiate between norm behavior and abnormalbehavior.
 14. The method according to claim 14 wherein the learningcomprises receiving normal behavior examples from a qualified data setachieved using a healthy too and a qualified measurement process; andreceiving a training set for abnormal behavior generated by performingat least one out of (a) deliberately shifting one or more model fixedparameters could, or (b) deliberately skewing measured values in modessimilar to known hardware issues.
 15. A monitoring system forcontrolling measurements of parameters of patterned structures, thesystem comprising: (a) data input utility for receiving data indicativeof measurements in at least one patterned structure; wherein the dataindicative of the measurements is measurement health data; (b) a memoryutility; and (c) a processor utility for applying at least one selectedverification mode to said data indicative of measurements said at leastone verification mode, wherein the applying comprises: analyzing thedata based on at least one predetermined factor and classifyingcorresponding measurement result as acceptable or unacceptable, therebyenabling to determine whether one or more of the measurements providingsaid unacceptable result are to be disregarded, or whether one or moreparameters of the predetermined fitting model are to be modified. 16.The measurement system of claim 15, wherein the measurement health datacomprises measurement health indicators that cover both (a) hardwareperformance quality and (b) a mismatch between a sample that comprisesthe patterned structure and a measurement recipe that was applied duringthe measurements in at least one patterned structure.
 17. Themeasurement system of claim 15, wherein the measurement health datacomprises measurement health indicators that are indicative of an impacton metrology reliability.
 18. The measurement system of claim 15,wherein the measurement health data comprises measurement healthindicators that characterize a measurement system during an execution ofthe method.
 19. The measurement system of claim 18, wherein themeasurement health indicators characterize at least one out ofillumination intensity and stability.
 20. The measurement systemaccording to claim 15, wherein the processor utility is configured toclassify the corresponding measurement result as being indicative of anerror when a quality score deteriorates below pre-defined value.